{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载数据\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv(\"usa_housing_price.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>size</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.317139</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>188.214212</td>\n",
       "      <td>1.059034e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>4.997100</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>160.042526</td>\n",
       "      <td>1.505891e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.134110</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>227.273544</td>\n",
       "      <td>1.058988e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>3.811764</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>164.816630</td>\n",
       "      <td>1.260617e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.959445</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>161.966659</td>\n",
       "      <td>6.309435e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0       79545.45857             5.317139                   7.009188   \n",
       "1       79248.64245             4.997100                   6.730821   \n",
       "2       61287.06718             5.134110                   8.512727   \n",
       "3       63345.24005             3.811764                   5.586729   \n",
       "4       59982.19723             5.959445                   7.839388   \n",
       "\n",
       "   Area Population        size         Price  \n",
       "0      23086.80050  188.214212  1.059034e+06  \n",
       "1      40173.07217  160.042526  1.505891e+06  \n",
       "2      36882.15940  227.273544  1.058988e+06  \n",
       "3      34310.24283  164.816630  1.260617e+06  \n",
       "4      26354.10947  161.966659  6.309435e+05  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#展示散点图\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure( figsize = (10,10))\n",
    "fig1 = plt.subplot(231)\n",
    "plt.scatter(data.loc[: , 'Avg. Area Income'],data.loc[: , 'Price'])\n",
    "plt.title(\"Price VS Income\")\n",
    "\n",
    "fig1 = plt.subplot(232)\n",
    "plt.scatter(data.loc[: , 'Avg. Area House Age'],data.loc[: , 'Price'])\n",
    "plt.title(\"Price VS House Age\")\n",
    "\n",
    "fig3 =plt.subplot(233)\n",
    "plt.scatter(data.loc[:,'Avg. Area Number of Rooms'],data.loc[:,'Price'])\n",
    "plt.title('Price VS Number of Rooms')\n",
    "\n",
    "fig4 =plt.subplot(234)\n",
    "plt.scatter(data.loc[:,'Area Population'],data.loc[:,'Price'])\n",
    "plt.title('Price VS Area Population')\n",
    "\n",
    "fig5 =plt.subplot(235)\n",
    "plt.scatter(data.loc[:,'size'],data.loc[:,'Price'])\n",
    "plt.title('Price VS size')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.059034e+06\n",
       "1    1.505891e+06\n",
       "2    1.058988e+06\n",
       "3    1.260617e+06\n",
       "4    6.309435e+05\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.loc[: , 'size']\n",
    "y = data.loc[: , \"Price\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(x).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "LR1 = LinearRegression()\n",
    "LR1.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1276881.85636623 1173363.58767144 1420407.32457443 ... 1097848.86467426\n",
      " 1264502.88144558 1131278.58816273]\n"
     ]
    }
   ],
   "source": [
    "y_predict_1 = LR1.predict(X)\n",
    "print(y_predict_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可视化 评估结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108771672553.6264 0.1275031240418234\n"
     ]
    }
   ],
   "source": [
    "from  sklearn.metrics import mean_squared_error,r2_score\n",
    "MSE_1 = mean_squared_error(y,y_predict_1)\n",
    "R2_1 = r2_score(y,y_predict_1)\n",
    "print(MSE_1,R2_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_reslut = plt.figure(figsize = (8,5))\n",
    "plt.scatter(X,y)\n",
    "plt.plot(X,y_predict_1,\"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多因子预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.45857</td>\n",
       "      <td>5.317139</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>23086.80050</td>\n",
       "      <td>188.214212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.64245</td>\n",
       "      <td>4.997100</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>40173.07217</td>\n",
       "      <td>160.042526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.06718</td>\n",
       "      <td>5.134110</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>36882.15940</td>\n",
       "      <td>227.273544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.24005</td>\n",
       "      <td>3.811764</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>34310.24283</td>\n",
       "      <td>164.816630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.19723</td>\n",
       "      <td>5.959445</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>26354.10947</td>\n",
       "      <td>161.966659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>60567.94414</td>\n",
       "      <td>3.169638</td>\n",
       "      <td>6.137356</td>\n",
       "      <td>22837.36103</td>\n",
       "      <td>161.641403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>78491.27543</td>\n",
       "      <td>4.000865</td>\n",
       "      <td>6.576763</td>\n",
       "      <td>25616.11549</td>\n",
       "      <td>159.164596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>63390.68689</td>\n",
       "      <td>3.749409</td>\n",
       "      <td>4.805081</td>\n",
       "      <td>33266.14549</td>\n",
       "      <td>139.491785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>68001.33124</td>\n",
       "      <td>5.465612</td>\n",
       "      <td>7.130144</td>\n",
       "      <td>42625.62016</td>\n",
       "      <td>184.845371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>65510.58180</td>\n",
       "      <td>5.007695</td>\n",
       "      <td>6.792336</td>\n",
       "      <td>46501.28380</td>\n",
       "      <td>148.589423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0          79545.45857             5.317139                   7.009188   \n",
       "1          79248.64245             4.997100                   6.730821   \n",
       "2          61287.06718             5.134110                   8.512727   \n",
       "3          63345.24005             3.811764                   5.586729   \n",
       "4          59982.19723             5.959445                   7.839388   \n",
       "...                ...                  ...                        ...   \n",
       "4995       60567.94414             3.169638                   6.137356   \n",
       "4996       78491.27543             4.000865                   6.576763   \n",
       "4997       63390.68689             3.749409                   4.805081   \n",
       "4998       68001.33124             5.465612                   7.130144   \n",
       "4999       65510.58180             5.007695                   6.792336   \n",
       "\n",
       "      Area Population        size  \n",
       "0         23086.80050  188.214212  \n",
       "1         40173.07217  160.042526  \n",
       "2         36882.15940  227.273544  \n",
       "3         34310.24283  164.816630  \n",
       "4         26354.10947  161.966659  \n",
       "...               ...         ...  \n",
       "4995      22837.36103  161.641403  \n",
       "4996      25616.11549  159.164596  \n",
       "4997      33266.14549  139.491785  \n",
       "4998      42625.62016  184.845371  \n",
       "4999      46501.28380  148.589423  \n",
       "\n",
       "[5000 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_multi = data.drop([\"Price\"],axis=1)\n",
    "X_multi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR_multi = LinearRegression()\n",
    "LR_multi.fit(X_multi,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_multi = LR_multi.predict(X_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10219846512.177862 0.9180229195220739\n"
     ]
    }
   ],
   "source": [
    "MSE_multi  = mean_squared_error(y,y_predict_multi )\n",
    "R2_multi  = r2_score(y,y_predict_multi )\n",
    "print(MSE_multi ,R2_multi )\n",
    "#R2结果趋近于1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#多因子\n",
    "fig_reslut_multi = plt.figure(figsize = (8,5))\n",
    "plt.scatter(y,y_predict_multi)\n",
    "plt.show()\n",
    "#单因子\n",
    "fig_reslut_multi2 = plt.figure(figsize = (8,5))\n",
    "plt.scatter(y,y_predict_1)\n",
    "plt.show()\n",
    "#多因子效果更好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[65000     5     5 30000   200]]\n"
     ]
    }
   ],
   "source": [
    "X_test = [65000,5,5,30000,200]\n",
    "X_test = np.array(X_test).reshape(1,-1)\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[817052.19516299]\n"
     ]
    }
   ],
   "source": [
    "y_test = LR_multi.predict(X_test)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "pycharm-bd849f12",
   "language": "python",
   "display_name": "PyCharm (Learning)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}